August 2, 2021

Boosting is an additive model, but is different from generalized additive model, in which each weak learner only involves one variable and p number of functions are used and added up. Boosting is also different from random forests, another additive model. In random forests, each tree is generated independently, so they can’t borrow information from each other. Adaboost is a special case of this framework with exponential loss for classification.